Operations Research for Supply Chain Management

A special issue of Computation (ISSN 2079-3197). This special issue belongs to the section "Computational Engineering".

Deadline for manuscript submissions: closed (15 May 2023) | Viewed by 2011

Special Issue Editors


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Guest Editor
Faculty of Economics, Department of Research Methods of Management, Maria Curie Sklodowska University, 20-031 Lublin, Poland
Interests: game theory; analytical methods; data analysis; quantitative research; teaching and project management
Department of Civil and Environmental Engineering, Cullen College of Engineering, University of Houston, Katy Academic Building, 22400 Grand Circle Blvd., Suite 307, Katy, Houston, TX 77449, USA
Interests: civil engineering; structural engineering; industrial engineering; supply chain management

Special Issue Information

Dear Colleagues,

Operations research widely applies existing scientific and technological knowledge and mathematical methods to solve specific problems in supply chain management and provides a basis for decision makers to choose the best decision.

As businesses become global, supply chain management research has become widely popular. Operations research is viewed as a science focused on applying mathematical modeling and analysis to help in decision-making and, as a consequence, to increase the efficiency of the supply chain. However, there are still some interesting and challenging problems in technology and methods that are worth further exploring.

Therefore, this Special Issue aims to bring together original research and review articles discussing the latest developments in data-driven operations research in supply chain management. We welcome submissions that present new ideas and discuss the future of operations research in supply chain management.

It is our pleasure to invite authors to contribute to this Special Issue by submitting relevant research articles that will be subject to rigorous peer-review.

Specific methods and fields of applications include but are not limited to the following:

  • Statistical methods in supply chain management;
  • Data-driven supply chain management;
  • Group decision-making analysis in supply chain management;
  • Multi-criteria decision analysis and application in supply chain management;
  • Robust optimization in supply chain management;
  • Stochastic optimization in supply chain management;
  • Fuzzy programming in supply chain management.

Dr. Anna Tatarczak
Dr. Mahdi Safa
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Computation is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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20 pages, 2850 KiB  
Article
The Differential Evolution Algorithm for Solving the Problem of Size Selection and Location of Infectious Waste Incinerator
by Thitiworada Srisuwandee, Sombat Sindhuchao and Thitinon Srisuwandee
Computation 2023, 11(1), 10; https://doi.org/10.3390/computation11010010 - 8 Jan 2023
Cited by 4 | Viewed by 1552
Abstract
The disposal of infectious waste remains one of the most severe medical, social, and environmental problems in almost every country. Choosing the right location and arranging the most suitable transport route is one of the main issues in managing hazardous waste. Identifying a [...] Read more.
The disposal of infectious waste remains one of the most severe medical, social, and environmental problems in almost every country. Choosing the right location and arranging the most suitable transport route is one of the main issues in managing hazardous waste. Identifying a site for the disposal of infectious waste is a complicated process because both tangible and intangible factors must be considered together, and it also depends on various rules and regulations. This research aims to solve the problem of the size selection and location of infectious waste incinerators for 109 community hospitals in the upper part of northeastern Thailand by applying a differential evolution algorithm to solve the problem with the objective of minimizing the total system cost, which consists of the cost of transporting infectious waste, the fixed costs, and the variable cost of operating the infectious waste incinerator. The developed differential evolution produces vectors that differ from the conventional differential evolution. Instead of a single set of vectors, three are created to search for the solution. In addition to solving the problem of the case study, this research conducts numerical experiments with randomly generated data to measure the performance of the differential evolution algorithm. The results show that the proposed algorithm efficiently solves the problem and can find the global optimal solution for the problem studied. Full article
(This article belongs to the Special Issue Operations Research for Supply Chain Management)
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